scholarly journals Effects of Variable Weather Conditions on Baled Proportion of Varied Amounts of Harvestable Cereal Straw, Based on Simulations

2021 ◽  
Vol 13 (16) ◽  
pp. 9449
Author(s):  
Alfredo de Toro ◽  
Carina Gunnarsson ◽  
Nils Jonsson ◽  
Martin Sundberg
Keyword(s):  
Standard Deviation ◽  
Moisture Content ◽  
Annual Variation ◽  
Weather Conditions ◽  
Weather Data ◽  
Cereal Straw ◽  
Grain Moisture ◽  
Machine Systems ◽  
Variable Weather

All harvestable cereal straw cannot be collected every year in regions where wet periods are probable during the baling season, so some Swedish studies have used 'recovery coefficients’ to estimate potential harvestable amounts. Current Swedish recovery coefficients were first formulated by researchers in the early 1990s, after discussions with crop advisors, but there are no recent Swedish publications on available baling times and recovery proportions. Therefore, this study evaluated baling operations over a series of years for representative virtual farms and machine systems in four Swedish regions, to determine the available time for baling, baled straw ratio and annual variation in both. The hourly grain moisture content of pre-harvested cereals and swathed straw was estimated using moisture models and real weather data for 22/23 years, and the results were used as input to a model for simulating harvesting and baling operations. Expected available baling time during August and September was estimated to be 39–49%, depending on region, with large annual variation (standard deviation 22%). The average baling coefficient was estimated to be 80–86%, with 1400 t·year−1 harvestable straw and 15 t·h−1 baling capacity, and the annual variation was also considerable (s.d. 20%).

Relationship Between Pavement Temperature and Weather Data: Wisconsin Field Study to Verify Superpave Algorithm

1998 ◽  
Vol 1609 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Peter J. Bosscher ◽  
Hussain U. Bahia ◽  
Suwitho Thomas ◽  
Jeffrey S. Russell
Keyword(s):  
Standard Deviation ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Test Site ◽  
Temperature Data ◽  
Pavement Design ◽  
Asphalt Binders ◽  
Weather Data ◽  
Air Temperatures ◽  
Pavement Temperature

Six test sections were constructed on US-53 in Trempealeau County by using different performance-graded asphalt binders to validate the Superpave pavement temperature algorithm and the binder specification limits. Field instrumentation was installed in two of the test sections to monitor the thermal behavior of the pavement as affected by weather. The instrumentation was used specifically to monitor the temperature of the test sections as a function of time and depth from the pavement surface. A meteorological station was assembled at the test site to monitor weather conditions, including air temperature. Details of the instrumentation systems used and analysis of the data collected during the first 22 months of the project are presented. The analysis was focused on development of a statistical model for estimation of low and high pavement temperatures from meteorological data. The model was compared to the Superpave recommended model and to the more recent model recommended by the Long-Term Pavement Performance (LTPP) program. The temperature data analysis indicates a strong agreement between the new model and the LTPP model for the estimation of low pavement design temperature. However, the analysis indicates that the LTPP and Superpave models underestimate the high pavement design temperature at air temperatures higher than 30°C. The temperature data analyses also indicate that there are significant differences between the standard deviation of air temperatures and the standard deviation of the pavement temperatures. These differences raise some questions about the accuracy of the reliability estimates used in the current Superpave recommendations.

scholarly journals Effect of sowing technology on the yield and harvest grain moisture content of maize (Zea mays L.) hybrids with different genotypes

2016 ◽  
pp. 17-22
Author(s):  
Eszter Murányi
Keyword(s):  
Moisture Content ◽  
Grain Yield ◽  
Plant Density ◽  
Research Work ◽  
Field Research ◽  
Weather Conditions ◽  
Research Centre ◽  
Row Spacing ◽  
Grain Moisture ◽  
Significant Difference

From the aspect of the efficiency of maize production harvest grain moisture content shall be considered beside the amount of harvested grain yield. Hybrids with different genotypes and vegetation period length lose their moisture content different that is affected by row spacing and plant density – among agrotechnical production factors – depending on the given crop year. In the present research work three crop years with different weather conditions were studied (2013, 2014, and 2015). The small-plot field experiment was set up at the Látókép Field Research Centre of the University of Debrecen, Centre for Agricultural Sciences with four replications on a chernozem soil type. The effect of three factors was analysed in the experiment on yield amount and its moisture content. Factors were row spacing (45 and 76 cm), plant density (50, 70 and 90 thousand plants ha-1), while hybrids were of very early (Sarolta: FAO 290), early (DKC 4014: FAO 320, P 9175: FAO 330, P 9494: FAO 390) and medium (SY Afinity: FAO 470) ripening. In the crop year of 2013 the highest yield was produced – regarding the average of the hybrids – by the application of a row spacing of 45 cm (4.5%, 673 kg ha-1), however there was no significant difference between the yield of the populations of different row spacings. Significant difference (14.9%, 1751 kg ha-1; 6.3%, 583 kg ha-1) could be found in case of yield between different row spacing applications in 2014 and 2015. The effect of insufficiently distributed low amount of precipitation and lasting heat days in 2015 could be revealed in yield amounts and harvest grain yield moisture content results that were lower than in the previous years. In 2015 grain yield moisture content varied between 10.3 and 13.9% in case of a row spacing of 45 cm, while by 76 cm between 11.0 and 13.9%.

scholarly journals Moisture content of cereals at harvesting time by comparing microclimate values and standard weather data

1993 ◽  
Vol 41 (3) ◽  
pp. 167-178
Author(s):  
A.J. Atzema
Keyword(s):  
Moisture Content ◽  
Surface Temperature ◽  
Air Temperature ◽  
Weather Conditions ◽  
Dew Point ◽  
Weather Data ◽  
Daily Cycle ◽  
The Difference ◽  
High Equilibrium ◽  
Screen House

The moisture content of wheat and barley together with the weather elements were measured at 3 different experimental sites in the Netherlands in 1990-91. The difference in the dew point temperature in the screen[house] and in the field was small. However, the differences between air temperature in the screen and those at different heights in wheat and in barley stands were considerable. In daytime the surface temperature of barley was higher than that of wheat under the same weather conditions as a result of a higher absorbtion coefficient. Both for wheat and barley, the maximum difference between the calculated moisture content was 0.5%, using the air temperature at 1.5 m height from the nearest standard weather station and the surface temperature of the spikes. Barley had a greater daily cycle in the moisture content of the grains than wheat as a result of a high equilibrium moisture content during the night and a low one in daytime.

Analysis of the moisture content of maize kernels in over-ripe plants

2006 ◽  
Vol 54 (4) ◽  
pp. 425-430
Author(s):  
T. Árendás ◽  
L. C. Marton ◽  
P. Bónis ◽  
Z. Berzsenyi
Keyword(s):  
Linear Regression ◽  
Moisture Content ◽  
Grain Yield ◽  
Equilibrium Moisture Content ◽  
Weather Conditions ◽  
Positive Correlation ◽  
Maize Grain Yield ◽  
Maize Grain ◽  
Maize Kernels

The effect of varying weather conditions on the moisture content of the maize grain yield was investigated in Martonvásár, Hungary from late August to late September, and from the 3rd third of September to the 1st third of Novemberbetween 1999 and 2002. In every year a close positive correlation (P=0.1%) could be observed between the moisture content in late September and the rate of drying down in October. Linear regression was used each year to determine the equilibrium moisture content, to which the moisture content of kernels returned if they contained less than this quantity of water in late September and harvesting was delayed. In the experimental years this value ranged from 15.24-19.01%.

Effects of Non-uniform Kernel Moisture Content on Moisture Measurement of Corn

2005 ◽  
Vol 1 (1) ◽  
pp. 77-93
Author(s):  
Bíborka Gillay ◽  
David B. Funk
Keyword(s):  
Moisture Content ◽  
Moisture Measurement ◽  
Safe Storage ◽  
Grain Moisture ◽  
Moisture Meter ◽  
Percent Moisture ◽  
New Crop ◽  
The U.S

The price paid for corn is usually based on 15.0 or 15.5 percent moisture content. However, corn must be dried below 13 percent moisture to ensure safe storage for a year or more. In the U.S., such stored corn cannot be directly remoistened before selling it, but it can be mixed with moist new-crop corn. Accurate moisture measurement of mixtures of dry and moist corn is important to permit adjustment of blending ratios to maximize profitability, but grain moisture meters are less accurate for mixtures of wet and dry grain. This research evaluated the differences between dielectric-type moisture meter results for mixed and equilibrated corn samples at different moisture levels and different measurement frequencies. Equilibrated grain samples tended to give lower moisture results than recently mixed grain samples - especially in the 1 to 10 MHz region. These differences permitted detection of mixtures by using moisture measurements at two frequencies.

PARAMETER OPTIMIZATION AND EXPERIMENTAL RESEARCH ON THE HAMMER MILL 锤片式粉碎机参数优化与试验研究

2020 ◽  
pp. 341-350
Author(s):  
Di Wang ◽  
Changbin He ◽  
Haiqing Tian ◽  
Liu Fei ◽  
Zhang Tao ◽  
...  
Keyword(s):  
Moisture Content ◽  
Orthogonal Experiment ◽  
Electricity Consumption ◽  
Spindle Speed ◽  
Hammer Mill ◽  
Factors Affecting ◽  
Evaluation Indexes ◽  
Grain Moisture ◽  
Validation Experiment ◽  
Corn Grain

Low productivity and high electricity consumption are considered problems of the hammer mill, which is widely used in current feed production. In this paper, the mechanical properties of corn grain ground by a hammer mill were analysed, and the key factors affecting the performance of the hammer mill were determined. The single-factor experiment and three-factor, three-level quadratic regression orthogonal experiment were carried out with the spindle speed, corn grain moisture content and number of hammers as experimental factors and the productivity and electricity consumption per ton as evaluation indexes. The results showed that the order of influence on the productivity was spindle speed > corn grain moisture content > number of hammers and that the order of influence on the electricity consumption per ton was corn grain moisture content > spindle speed > number of hammers. The parameters were optimized based on the response surface method with the following results: the spindle speed was 4306 r/min, the corn grain moisture content was 10%, and the number of hammers was 24. The validation experiment was carried out with the optimal parameters’ combination. The productivity and electricity consumption per ton were 988.12 kg/h and 5.37 kW·h/t, respectively, which were consistent with the predicted results of the model.

Effects of Grain Moisture Content on Mechanical Grain Harvesting Quality of Summer Maize

2018 ◽  
Vol 44 (12) ◽  
pp. 1747 ◽  
Author(s):  
Lu-Lu LI ◽  
Jun XUE ◽  
Rui-Zhi XIE ◽  
Ke-Ru WANG ◽  
Bo MING ◽  
...  
Keyword(s):  
Moisture Content ◽  
Summer Maize ◽  
Grain Moisture

scholarly journals Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3030
Author(s):  
Simon Liebermann ◽  
Jung-Sup Um ◽  
YoungSeok Hwang ◽  
Stephan Schlüter
Keyword(s):  
Neural Network ◽  
Goodness Of Fit ◽  
Renewable Energy Sources ◽  
Weather Conditions ◽  
Solar Irradiation ◽  
Weather Data ◽  
Short Term ◽  
Climate Conditions ◽  
Lstm Network ◽  
The Time Domain

Due to the globally increasing share of renewable energy sources like wind and solar power, precise forecasts for weather data are becoming more and more important. To compute such forecasts numerous authors apply neural networks (NN), whereby models became ever more complex recently. Using solar irradiation as an example, we verify if this additional complexity is required in terms of forecasting precision. Different NN models, namely the long-short term (LSTM) neural network, a convolutional neural network (CNN), and combinations of both are benchmarked against each other. The naive forecast is included as a baseline. Various locations across Europe are tested to analyze the models’ performance under different climate conditions. Forecasts up to 24 h in advance are generated and compared using different goodness of fit (GoF) measures. Besides, errors are analyzed in the time domain. As expected, the error of all models increases with rising forecasting horizon. Over all test stations it shows that combining an LSTM network with a CNN yields the best performance. However, regarding the chosen GoF measures, differences to the alternative approaches are fairly small. The hybrid model’s advantage lies not in the improved GoF but in its versatility: contrary to an LSTM or a CNN, it produces good results under all tested weather conditions.

Sign in / Sign up

Export Citation Format

Share Document